International Business Machines Corporation

Clara Liu

Pedro Miguel Barbas

Enzo Cialini

David Kelly

Manohar Bashyam

Qinlong Luo

A method of accessing objects with fine-grained access control (FGAC) in a relational database management system (RDBMS) storing a segmented column-major database. For each object with access restrictions, an artificial neural network (ANN), is trained by generating an equally distributed segment map of segmented data entries, so that the map reproduces the row disposition in the unsegmented object. When a user access request is received, these ANNs are referred to determine if any of the objects to be accessed are subject to access restrictions. If that is the case, then the ANN creates a pseudo-view construct of its associated object which is limited to data entries that the user has permission to access. The pseudo-views are then injected into the user access request to embed the fine-grained access controls for subsequent processing of the request, which can then proceed without further regard to user-specific access restrictions.

Fine-grained access control of column-major relational database management systems

Embodiments of the present invention provide systems and methods for associating rule mining in data comprising first and second variables, by generating a first and second filtering bitmap. The first filtering bitmap represents a set of values for the second variable for each of a plurality of values of the first variable. The second filtering bitmap represents, associations between frequent values of the second variable, which enables the identification of frequent 2-pattern itemsets. Thus, by performing first logical operations on the frequent 2-pattern itemsets, frequent high order-pattern itemsets may be identified. A max pattern itemset may be identified among the frequent high order-pattern itemsets. As a result, embodiments may mitigate performance and stability problems associated with conventional association rule mining approaches.

Association rule mining using max pattern transactions

Embodiments of the present invention provide systems and methods for associating rule mining. By way of example, embodiments obviate or mitigate performance and stability problems associated with conventional association rule mining approaches, by proposing concepts implementing association rule mining using rolling filtering bitmaps over dynamic data.

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Markham, Canada

Clara Liu